Project Summary

Project Abstract:
Safety performance at signalized intersections is an outcome of complex interactions among several contributing factors including different signal operations, drivers’ behavior, and vehicular performance. Crash-based evaluation commonly used in safety analysis is hampered by several shortcomings such as randomness and rarity of crash occurrences and lack of timeliness. This is particularly the case for safety evaluation of technology-driven safety improvement projects that are frequently updated or replaced by newer ones before it is possible to gather adequate crash data for a reliable and defensible before-after evaluation. A traditional alternative to crash-based analyses relies on surrogate safety measures. However, the current practice of collecting surrogate data is subject to inherent subjectivity of humans involved and prohibitive cost of data reduction efforts. Connected vehicle technology is a platform that allows the vehicles to transmit information to one another and to the infrastructure wirelessly. It provides a connected, data-rich, real-time data environment from equipment located on-board vehicles and within the infrastructure that can be used to provide historical and real-time vehicle trajectory and signal phase and timing (SPaT) status; thus enabling the capability to monitor as well as predict the sequence of events. This project will explore safety indicators that can be derived from both real-time and historical connected vehicle data. The project will focus on information that can be extracted from message sets defined by the Dedicated Short Range Communications (DSRC) J2735 standard. Then, the research team will develop a methodological framework to evaluate the ability of the indicator(s) to detect safety deficiency of intersection operation. Finally, the research team will conduct proof-of-concept testing of the developed framework using a simulation test bed.

Project Objectives:
The ultimate goal of this project is to advance the state-of-the-art in safety performance monitoring at signalized intersections using the data-rich connected vehicle framework. This project will first explore safety indicators that can be derived from both real-time and historical connected vehicle data. The project will focus on information that can be extracted from message exchange under the DSRC J2735 standard particularly on the Basic Safety Message (BSM), Map Data (MAP), and Signal Phase and Timing (SPaT). Then, the research team will develop a methodological framework to evaluate the ability of the indicator(s) to detect safety deficiency of intersection operation. Finally, the research team will conduct proof-of-concept testing of the developed framework using a simulation test bed.

Task Descriptions:
Task 1: Update Literature Review and Identify Potential Safety Indicators.
The researchers are aware of the ongoing development of various aspects of connected vehicle technology and will keep the literature up to date through available resources and active participation in relevant projects. Three key topics that will be extensively reviewed are:

Connected vehicle applications. The focus will be on the data exchange, communications and current limitations in both safety and mobility applications that utilize the vehicle state in conjunction with the infrastructure status.

DSRC J2735 standard message sets. This includes the latest modifications to the SPaT standard which is an ongoing work by Battelle/TTI team.

Safety performance measures. The focus would be primarily on the measures that can be extracted at signalized intersections and do not require extensive market penetration of connected vehicles. Examples of these measures include rates of vehicles trapped in the dilemma zone, traffic conflicts, and erratic maneuvers.

Safety evaluation methods. The focus of the review would be on latest studies on techniques to compare safety indicators observed at multiple sites and how to interpret the comparative safety performance of the observed measures.

The researchers will explore potential safety indicators that can be extracted from different message sets. From these sets of indicators, the researchers will conduct a comprehensive examination of the feasibility of extracting each measure under the connected vehicle framework. Then, these measures will be qualitatively evaluated and prioritized based on various dimensions such as their causal relationships with crashes, ease of extraction, and operational requirements.

Task 2: Develop Algorithms to Extract Safety Indicators.
Based on the identified indicators in the first task, the researchers will develop algorithms to extract these measures. It is anticipated the algorithms will involve:

In the process of the development, the researchers will favor the algorithms that consume minimal data bandwidth and require the least computational overhead in order to be capable of processing and extracting the relevant information in real time. The impacts of factors such as communication failure and varying market penetration will be qualitatively considered during the development as well.

Task 3: Develop Evaluation Methodology and Proof-of-Concept Test Plan
The researchers will develop appropriate theoretical framework to utilize these indicators to quantify safety performance. The framework developed will enhance traditional evaluation procedure by replacing and/or supplementing the crash-based analysis with measures obtained from connected vehicle platform. The researchers will then develop a test plan to evaluate the algorithms and proposed evaluation framework using a simulation test bed. The test plan will address:

Geometric and operational characteristics of intersection test bed.

Perturbations in operational and/or geometric characteristics that will influence the safety characteristics of the intersection operations. The proposed evaluation methodology should be able to detect degrading safety performance when suboptimal designs are introduced to the network (e.g. shortened clearance intervals).

Safety indicators to be extracted under connected vehicle frame work.

Sensitivity analysis of safety indicators with respect to potential impact factors such as market penetration, communication failure, and stochastic characteristics of the data sets.

Other supporting measures of effectiveness that can be extracted or derived from the simulation.

Experimental simulation runs to capture the effects of factors of interest.

Task 4: Prepare Proof-of-Concept Simulation Test Bed
Using the test plan prepared in the previous task, the simulation test bed will be developed. It is anticipated that VISSIM simulation will be used to prepare the test bed. VISSIM has the capability to model complex interactions between vehicles and signals and allows customization/add-ons to the model via various alternatives. In addition, it also has the base support for the connected vehicle data exchange model via its C2X application programming interface (API). Connected vehicle capability and safety assessment features will be implemented using these customization capability of VISSIM.

Task 5: Conduct Proof-of-Concept Simulation and Evaluate Methodology.
It is envisioned that the proposed methodology will be demonstrated for a potential set of indicators on selected operational scenarios using the developed test bed. A series of simulation runs will be executed according the test plan. The data outputs from the simulation will be post-processed and analyzed. By using simulation, the researchers will be able to validate the proposed method by examining its capability to detect the changes in safety performance from the intentional perturbation of simulated transportation facilities, quantify the sensitivity of the proposed methods, and evaluate the effectiveness of safety performance monitoring under connected vehicle framework.

Task 6: Prepare the Final Report.
The research team will prepare the final report that documents the research problem, approach, methodology, findings, conclusions, and recommendations from the study.

Implementation of Research Outcomes:Monitoring and evaluation of the safety performance at signalized intersections have traditionally relied on crash data. The crash-based approach is reactive in that crashes must occur before the analyses can be conducted. In addition, crashes are rare events and therefore often require years of data gathering for it to be sufficient for a reliable statistical analysis. An alternative approach to crash-based analyses relies on surrogate safety data which possess causal relationship with but are more frequent than crashes. Nevertheless, the current practice of collecting surrogate data at signalized intersections relies primarily on video recordings which require labor-intensive back-office processing and manual review.

The advent of connected vehicle technology allows vehicles to talk to each other and to infrastructure wirelessly. Through this platform, vehicle movements and signal status at the facilities can be automatically and continually monitored in real time.This project developed and evaluated a safety performance monitoring framework for signalized intersections using data exchange within vehicle-to-infrastructure platform. Safety indicators can be automatically extracted from the received communication message sets at the roadside unit. The evaluation results revealed that at least 40% market penetration rate is required for a reliable detection of safety deficiency under light to moderate traffic volume condition. In case of lower market penetration rates, observation period can be extended to compensate for smaller sample size.

Planned Presentation: Impacts of Market Penetration and Observation Period on Safety Performance Monitoring at Signalized Intersections Using Connected Vehicle Data, P. Songchitruksa, to be submitted for presentation at the 2015 Transportation Research Board Annual Meeting.

Software Scripts Developed: Researchers developed a simulation test bed in VISSIM that enables vehicle-to-infrastructure (V2I) communications. Related algorithms for extracting safety measures in the V2I communications environment were also developed using Python scripts.

Technique Developed: The project developed a framework to monitor the safety performance of signalized intersections using data exchange within connected vehicle environment. This framework can potentially be incorporated into facilities instrumented with connected vehicle capability. The connected vehicle application can be developed based on the proposed framework to automatically collect and evaluate the safety performance of signalized intersection operation.

Impacts/Benefits of Implementation:
The research results expect to introduce a novel and efficient approach for collecting critical safety data and expediting safety evaluation using emerging connected vehicle technology. As the technology adoption is expected to progress at a faster pace in the near future, it is envisioned that the proposed application can have immediate impact on detecting safety concerns, devising safety countermeasures, and allocating budget for safety improvements.